PARCOMPUTE = TRUE
N_CORE = parallel::detectCores()
In this notebook, we repeat the analysis of 02_temporal_heterogeneity.Rmd for all of our core indicators.
# Fetch the following sources and signals from the API
# TODO: Add Google Symptoms "eventually"
source_names = c("doctor-visits", "fb-survey", "fb-survey",
"hospital-admissions", "hospital-admissions")
signal_names = c("smoothed_adj_cli", "smoothed_cli", "smoothed_hh_cmnty_cli",
"smoothed_adj_covid19_from_claims", "smoothed_adj_covid19_from_claims")
pretty_names = c("Doctor visits", "Facebook CLI", "Facebook CLI-in-community",
"Hospitalizations", "Hospitalizations")
target_names = c("Cases", "Cases", "Cases", "Cases", "Deaths")
geo_level = "county"
start_day = "2020-04-15"
end_day = NULL
cache_fname = 'cached_data/10_heterogeneity_core_indicators.RDS'
if (!file.exists(cache_fname)) {
df_signals = vector("list", length(signal_names))
for (i in 1:length(signal_names)) {
df_signals[[i]] = suppressWarnings(
covidcast_signal(source_names[i], signal_names[i],
start_day, end_day,
geo_type=geo_level))
}
# Fetch USAFacts confirmed case incidence proportion (smoothed with 7-day
# trailing average)
df_cases = suppressWarnings(
covidcast_signal("usa-facts", "confirmed_7dav_incidence_prop",
start_day, end_day,
geo_type=geo_level))
df_deaths = suppressWarnings(
covidcast_signal("usa-facts", "deaths_7dav_incidence_prop",
start_day, end_day,
geo_type=geo_level))
saveRDS(list(df_signals, df_cases, df_deaths), cache_fname)
} else {
cached_data = readRDS(cache_fname)
df_signals = cached_data[[1]]
df_cases = cached_data[[2]]
df_deaths = cached_data[[3]]
}
case_num = 500
geo_values = suppressWarnings(covidcast_signal("usa-facts", "confirmed_cumulative_num",
max(df_cases$time_value),
max(df_cases$time_value))) %>%
filter(value >= case_num) %>% pull(geo_value)
## Fetched day 2020-11-20: 1, success, num_entries = 3192
geo_values = suppressWarnings(covidcast_signal("usa-facts", "confirmed_cumulative_num",
'2020-11-01',
'2020-11-01')) %>%
filter(value >= case_num) %>% pull(geo_value)
## Fetched day 2020-11-01: 1, success, num_entries = 3192
sensorize_time_ranges = list(
c(-7, -1),
c(-10, -1),
c(-14, -1),
c(-21, -1),
c(-14, -8),
c(-21, -8),
c(-28, -8),
c(-35, -8),
c(-42, -8))
for (ind_idx in 1:length(source_names)) {
if (target_names[ind_idx] == 'Cases') {
df_target = df_cases
} else if (target_names[ind_idx] == 'Deaths') {
df_target = df_deaths
} else {
stop(sprintf("No matching dataframe for target %s.", target_names[ind_idx]))
}
ind_df = tibble(df_signals[[ind_idx]]) %>% filter(geo_value %in% geo_values)
ind_target = inner_join(ind_df, tibble(df_target),
by=c('geo_value', 'time_value')) %>% select (
geo_value=geo_value,
time_value=time_value,
indicator_value=value.x,
target_value=value.y,
)
ind_global_sensorized = ind_target %>% group_by (
geo_value,
) %>% group_modify ( ~ {
fit = lm(target_value ~ indicator_value - 1, data =.x);
tibble(time_value=.x$time_value,
indicator_value=.x$indicator_value,
target_value=.x$target_value,
sensorized_value=fit$fitted.values)
}) %>% ungroup
df_global_sensorized = ind_global_sensorized %>% transmute (
geo_value=geo_value,
signal='ind_sensorized',
time_value=time_value,
direction=NA,
issue=lubridate::ymd('2020-11-01'),
lag=NA,
value=sensorized_value,
stderr=NA,
sample_size=NA,
data_source='linear_sensorization',
)
attributes(df_global_sensorized)$geo_type = 'county'
attributes(df_global_sensorized)$metadata$geo_type = 'county'
class(df_global_sensorized) = c("covidcast_signal", "data.frame")
base_cor_fname = sprintf('results/10_base_cors_%s_%s_%s.RDS',
source_names[ind_idx], signal_names[ind_idx],
target_names[ind_idx])
if (!file.exists(base_cor_fname)) {
df_cor_base_ind = covidcast_cor(df_signals[[ind_idx]], df_target,
by='time_value', method='spearman')
df_cor_sensorized_ind = covidcast_cor(df_global_sensorized, df_target,
by='time_value', method='spearman')
df_cor_base = rbind(df_cor_base_ind, df_cor_sensorized_ind)
df_cor_base$Indicator = as.factor(c(rep('Raw', nrow(df_cor_base_ind)),
rep('Sensorized (Spatial)',
nrow(df_cor_sensorized_ind))))
saveRDS(df_cor_base, base_cor_fname)
} else {
df_cor_base = readRDS(base_cor_fname)
}
sensorize_fname = sprintf('results/10_sensorize_cors_%s_%s_%s.RDS',
source_names[ind_idx], signal_names[ind_idx],
target_names[ind_idx])
sensorize_val_fname = sprintf('results/10_sensorize_vals_%s_%s_%s.RDS',
source_names[ind_idx], signal_names[ind_idx],
target_names[ind_idx])
if (!file.exists(sensorize_fname)) {
sensorize_cors = vector('list', length(sensorize_time_ranges))
ind_target_sensorized_list = vector('list', length(sensorize_time_ranges))
for (outer_idx in 1:length(sensorize_time_ranges)) {
sensorize_llim = sensorize_time_ranges[[outer_idx]][1]
sensorize_ulim = sensorize_time_ranges[[outer_idx]][2]
min_sensorize_date = lubridate::ymd(start_day) - sensorize_llim
max_sensorize_date = max(ind_target$time_value)
sensorize_date_offsets = 0:(max_sensorize_date-min_sensorize_date)
joiner_df_list = vector('list', length(sensorize_date_offsets))
for (idx in 1:length(sensorize_date_offsets)) {
dt = sensorize_date_offsets[idx]
sensorize_date = min_sensorize_date + dt
joiner_df_list[[idx]] = tibble(
sensorize_date = sensorize_date,
time_value = sensorize_date + sensorize_llim:sensorize_ulim)
}
joiner_df = bind_rows(joiner_df_list)
if (!PARCOMPUTE) {
ind_sensorized_lm = ind_target %>% full_join(
joiner_df,
on='time_value',
) %>% group_by (
geo_value,
sensorize_date,
) %>% group_modify (
~ broom::tidy(lm(target_value ~ indicator_value - 1, data = .x))
) %>% ungroup
} else {
ind_grouped_list = ind_target %>% full_join(
joiner_df,
on='time_value',
) %>% group_by (
geo_value,
sensorize_date,
) %>% group_split
ind_sensorized_lm = parallel::mclapply(ind_grouped_list, function(df) {
broom::tidy(
lm(target_value ~ indicator_value - 1, data = df)
) %>% mutate (
geo_value = unique(df$geo_value),
sensorize_date = unique(df$sensorize_date),
)}, mc.cores = N_CORE) %>% bind_rows
}
ind_sensorized_wide = ind_sensorized_lm %>% select(
geo_value,
sensorize_date,
term,
estimate,
) %>% mutate (
term = sapply(term, function(x) {ifelse(x=='(Intercept)',
'intercept',
'slope')}),
) %>% pivot_wider (
id_cols = c(geo_value, sensorize_date),
names_from=term,
values_from=estimate,
)
ind_target_sensorized = ind_target %>% inner_join (
ind_sensorized_wide,
by=c('time_value'='sensorize_date',
'geo_value'),
) %>% mutate (
sensorized_value = indicator_value * slope,
)
df_sensorized = ind_target_sensorized %>% transmute (
geo_value=geo_value,
signal='ind_sensorized',
time_value=time_value,
direction=NA,
issue=lubridate::ymd('2020-11-01'),
lag=NA,
value=sensorized_value,
stderr=NA,
sample_size=NA,
data_source='linear_sensorization',
)
attributes(df_sensorized)$geo_type = 'county'
class(df_sensorized) = c("covidcast_signal", "data.frame")
df_cor_sensorized_ind = covidcast_cor(df_sensorized, df_target,
by='time_value', method='spearman')
df_cor_sensorized_ind$Indicator = sprintf('Sensorized (TS, %d:%d)',
sensorize_llim,
sensorize_ulim)
sensorize_cors[[outer_idx]] = df_cor_sensorized_ind
ind_target_sensorized_list[[outer_idx]] = ind_target_sensorized
}
saveRDS(sensorize_cors, sensorize_fname)
saveRDS(ind_target_sensorized_list, sensorize_val_fname)
} else {
sensorize_cors = readRDS(sensorize_fname)
ind_target_sensorized_list = readRDS(sensorize_val_fname)
}
df_cor = bind_rows(df_cor_base, sensorize_cors)
df_cor$Indicator = stringr::str_replace(df_cor$Indicator,
'Sensorized ',
"")
df_cor$Indicator = factor(df_cor$Indicator,
levels=c('Raw',
"(Spatial)",
sapply(sensorize_time_ranges,
function(x) {
sprintf("(TS, %d:%d)",
x[[1]], x[[2]])
})))
plt = ggplot(df_cor, aes(x = time_value, y = value)) +
geom_line(aes(color = Indicator)) +
labs(title = sprintf("Correlation between %s and %s",
pretty_names[ind_idx],
target_names[ind_idx]),
subtitle = "Per day",
x = "Date", y = "Correlation") +
theme(legend.position = "bottom")
print(plt)
}
## Joining, by = "time_value"
## Joining, by = "time_value"
## Joining, by = "time_value"
## Joining, by = "time_value"
## Joining, by = "time_value"
## Joining, by = "time_value"
## Joining, by = "time_value"
## Joining, by = "time_value"
## Joining, by = "time_value"
## Warning: Removed 203 row(s) containing missing values (geom_path).
## Joining, by = "time_value"
## Joining, by = "time_value"
## Joining, by = "time_value"
## Joining, by = "time_value"
## Joining, by = "time_value"
## Joining, by = "time_value"
## Joining, by = "time_value"
## Joining, by = "time_value"
## Joining, by = "time_value"
## Warning: Removed 193 row(s) containing missing values (geom_path).
## Joining, by = "time_value"
## Joining, by = "time_value"
## Joining, by = "time_value"
## Joining, by = "time_value"
## Joining, by = "time_value"
## Joining, by = "time_value"
## Joining, by = "time_value"
## Joining, by = "time_value"
## Joining, by = "time_value"
## Warning: Removed 193 row(s) containing missing values (geom_path).
## Joining, by = "time_value"
## Joining, by = "time_value"
## Joining, by = "time_value"
## Joining, by = "time_value"
## Joining, by = "time_value"
## Joining, by = "time_value"
## Joining, by = "time_value"
## Joining, by = "time_value"
## Joining, by = "time_value"
## Warning: Removed 203 row(s) containing missing values (geom_path).
## Joining, by = "time_value"
## Joining, by = "time_value"
## Joining, by = "time_value"
## Joining, by = "time_value"
## Joining, by = "time_value"
## Joining, by = "time_value"
## Joining, by = "time_value"
## Joining, by = "time_value"
## Joining, by = "time_value"
## Warning: Removed 203 row(s) containing missing values (geom_path).
QUANTS = c(0.01, 0.99)
# TODO: Add more "core indicators"
for (ind_idx in 1:length(source_names)) {
if (target_names[ind_idx] == 'Cases') {
df_target = df_cases
} else if (target_names[ind_idx] == 'Deaths') {
df_target = df_deaths
} else {
stop(sprintf("No matching dataframe for target %s.", target_names[ind_idx]))
}
base_cor_fname = sprintf('results/10_base_cors_%s_%s_%s.RDS',
source_names[ind_idx], signal_names[ind_idx],
target_names[ind_idx])
sensorize_fname = sprintf('results/10_sensorize_cors_%s_%s_%s.RDS',
source_names[ind_idx], signal_names[ind_idx],
target_names[ind_idx])
sensorize_val_fname = sprintf('results/10_sensorize_vals_%s_%s_%s.RDS',
source_names[ind_idx], signal_names[ind_idx],
target_names[ind_idx])
df_cor_base = readRDS(base_cor_fname)
sensorize_cors = readRDS(sensorize_fname)
sensorized_vals = readRDS(sensorize_val_fname)
for (inner_idx in 1:length(sensorize_time_ranges)) {
sv = sensorized_vals[[inner_idx]]
print(summary(sv$slope))
print(slope_limits <- quantile(sv$slope, QUANTS, na.rm=TRUE))
plt = ggplot(
sensorized_vals[[inner_idx]],
aes(x=time_value,
y=slope),
) + geom_point (
alpha=0.1,
size=0.5,
) + geom_hline (
yintercept=0,
colour='white',
) + stat_summary (
aes(y=slope,
group=1,
colour='median'),
fun=median,
geom="line",
group=1,
) + stat_summary (
aes(y=slope,
group=1,
colour='+/- mad'),
fun=function(x) { median(x) + mad(x) },
geom="line",
group=1,
) + stat_summary (
aes(y=slope,
group=1,
colour='+/- mad'),
fun=function(x) { median(x) - mad(x) },
geom="line",
group=1,
) + scale_colour_manual(
values=c("median"="maroon",
"+/- mad"="darkgreen")
) + labs(
colour=''
) + ggtitle(
sprintf("Slope distribution for %s[%s], fitted on t in %d:%d",
pretty_names[ind_idx],
target_names[ind_idx],
sensorize_time_ranges[[inner_idx]][1],
sensorize_time_ranges[[inner_idx]][2])
) + ylim (
slope_limits[[1]], slope_limits[[2]]
)
print(plt)
}
}
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -78 1 3 5843 5 618993321
## 1% 99%
## 0.00000 37.79059
## Warning: Removed 3192 rows containing non-finite values (stat_summary).
## Warning: Removed 3192 rows containing non-finite values (stat_summary).
## Warning: Removed 3192 rows containing non-finite values (stat_summary).
## Warning: Removed 3192 rows containing missing values (geom_point).
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -50 1 3 6563 5 618993321
## 1% 99%
## 0.02170025 32.21366215
## Warning: Removed 6292 rows containing non-finite values (stat_summary).
## Warning: Removed 6292 rows containing non-finite values (stat_summary).
## Warning: Removed 6292 rows containing non-finite values (stat_summary).
## Warning: Removed 6292 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_hline).
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -26 1 3 3082 5 67193354
## 1% 99%
## 0.04686717 28.25974835
## Warning: Removed 6196 rows containing non-finite values (stat_summary).
## Warning: Removed 6196 rows containing non-finite values (stat_summary).
## Warning: Removed 6196 rows containing non-finite values (stat_summary).
## Warning: Removed 6196 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_hline).
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0 1 3 3321 5 67193354
## 1% 99%
## 0.07331203 24.59761395
## Warning: Removed 6008 rows containing non-finite values (stat_summary).
## Warning: Removed 6008 rows containing non-finite values (stat_summary).
## Warning: Removed 6008 rows containing non-finite values (stat_summary).
## Warning: Removed 6008 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_hline).
## Warning: Removed 32 row(s) containing missing values (geom_path).
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -78 1 3 1609 5 67193354
## 1% 99%
## 0.00000 37.49968
## Warning: Removed 3077 rows containing non-finite values (stat_summary).
## Warning: Removed 3077 rows containing non-finite values (stat_summary).
## Warning: Removed 3077 rows containing non-finite values (stat_summary).
## Warning: Removed 3077 rows containing missing values (geom_point).
## Warning: Removed 47 row(s) containing missing values (geom_path).
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -26 1 3 1893 5 67193354
## 1% 99%
## 0.04506428 27.75976171
## Warning: Removed 5968 rows containing non-finite values (stat_summary).
## Warning: Removed 5968 rows containing non-finite values (stat_summary).
## Warning: Removed 5968 rows containing non-finite values (stat_summary).
## Warning: Removed 5968 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_hline).
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0 1 3 1825 5 67193354
## 1% 99%
## 0.07214821 24.16930363
## Warning: Removed 5778 rows containing non-finite values (stat_summary).
## Warning: Removed 5778 rows containing non-finite values (stat_summary).
## Warning: Removed 5778 rows containing non-finite values (stat_summary).
## Warning: Removed 5778 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_hline).
## Warning: Removed 29 row(s) containing missing values (geom_path).
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0 1 3 1859 5 67193354
## 1% 99%
## 0.09155144 22.19500837
## Warning: Removed 5570 rows containing non-finite values (stat_summary).
## Warning: Removed 5570 rows containing non-finite values (stat_summary).
## Warning: Removed 5570 rows containing non-finite values (stat_summary).
## Warning: Removed 5570 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_hline).
## Warning: Removed 23 row(s) containing missing values (geom_path).
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0 1 3 1076 5 67193354
## 1% 99%
## 0.1133621 20.5456320
## Warning: Removed 5354 rows containing non-finite values (stat_summary).
## Warning: Removed 5354 rows containing non-finite values (stat_summary).
## Warning: Removed 5354 rows containing non-finite values (stat_summary).
## Warning: Removed 5354 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_hline).
## Warning: Removed 11 row(s) containing missing values (geom_path).
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -9.521 7.206 16.099 29.156 30.224 7784.272
## 1% 99%
## 0.1695286 220.0376289
## Warning: Removed 2922 rows containing non-finite values (stat_summary).
## Warning: Removed 2922 rows containing non-finite values (stat_summary).
## Warning: Removed 2922 rows containing non-finite values (stat_summary).
## Warning: Removed 2922 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_hline).
## Warning: Removed 58 row(s) containing missing values (geom_path).
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -1.775 6.878 15.259 25.090 28.028 4957.437
## 1% 99%
## 0.1914242 155.2232147
## Warning: Removed 2918 rows containing non-finite values (stat_summary).
## Warning: Removed 2918 rows containing non-finite values (stat_summary).
## Warning: Removed 2918 rows containing non-finite values (stat_summary).
## Warning: Removed 2918 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_hline).
## Warning: Removed 52 row(s) containing missing values (geom_path).
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -1.775 6.651 14.568 21.995 26.115 4672.902
## 1% 99%
## 0.2189535 110.5265133
## Warning: Removed 2884 rows containing non-finite values (stat_summary).
## Warning: Removed 2884 rows containing non-finite values (stat_summary).
## Warning: Removed 2884 rows containing non-finite values (stat_summary).
## Warning: Removed 2884 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_hline).
## Warning: Removed 50 row(s) containing missing values (geom_path).
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -1.775 6.553 13.957 19.055 24.373 4672.902
## 1% 99%
## 0.2735395 79.9243809
## Warning: Removed 2782 rows containing non-finite values (stat_summary).
## Warning: Removed 2782 rows containing non-finite values (stat_summary).
## Warning: Removed 2782 rows containing non-finite values (stat_summary).
## Warning: Removed 2782 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_hline).
## Warning: Removed 45 row(s) containing missing values (geom_path).
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -9.521 6.986 15.692 27.671 29.307 7784.272
## 1% 99%
## 0.1620832 204.7036593
## Warning: Removed 2768 rows containing non-finite values (stat_summary).
## Warning: Removed 2768 rows containing non-finite values (stat_summary).
## Warning: Removed 2768 rows containing non-finite values (stat_summary).
## Warning: Removed 2768 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_hline).
## Warning: Removed 56 row(s) containing missing values (geom_path).
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -1.775 6.474 14.225 21.115 25.391 4672.902
## 1% 99%
## 0.2193115 105.8992666
## Warning: Removed 2738 rows containing non-finite values (stat_summary).
## Warning: Removed 2738 rows containing non-finite values (stat_summary).
## Warning: Removed 2738 rows containing non-finite values (stat_summary).
## Warning: Removed 2738 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_hline).
## Warning: Removed 49 row(s) containing missing values (geom_path).
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -1.775 6.332 13.599 18.378 23.756 4672.902
## 1% 99%
## 0.2546758 77.1370595
## Warning: Removed 2655 rows containing non-finite values (stat_summary).
## Warning: Removed 2655 rows containing non-finite values (stat_summary).
## Warning: Removed 2655 rows containing non-finite values (stat_summary).
## Warning: Removed 2655 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_hline).
## Warning: Removed 38 row(s) containing missing values (geom_path).
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 6.36 13.40 17.16 22.99 2995.02
## 1% 99%
## 0.2973012 65.6272335
## Warning: Removed 2552 rows containing non-finite values (stat_summary).
## Warning: Removed 2552 rows containing non-finite values (stat_summary).
## Warning: Removed 2552 rows containing non-finite values (stat_summary).
## Warning: Removed 2552 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_hline).
## Warning: Removed 33 row(s) containing missing values (geom_path).
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 6.577 13.444 16.674 22.671 3082.796
## 1% 99%
## 0.3615449 59.5355723
## Warning: Removed 2440 rows containing non-finite values (stat_summary).
## Warning: Removed 2440 rows containing non-finite values (stat_summary).
## Warning: Removed 2440 rows containing non-finite values (stat_summary).
## Warning: Removed 2440 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_hline).
## Warning: Removed 29 row(s) containing missing values (geom_path).
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -0.3531 0.2245 0.4480 0.5400 0.7389 8.1778
## 1% 99%
## 0.00763222 2.09559199
## Warning: Removed 3002 rows containing non-finite values (stat_summary).
## Warning: Removed 3002 rows containing non-finite values (stat_summary).
## Warning: Removed 3002 rows containing non-finite values (stat_summary).
## Warning: Removed 3002 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_hline).
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.2273 0.4480 0.5361 0.7342 7.2829
## 1% 99%
## 0.009159536 2.034042046
## Warning: Removed 2966 rows containing non-finite values (stat_summary).
## Warning: Removed 2966 rows containing non-finite values (stat_summary).
## Warning: Removed 2966 rows containing non-finite values (stat_summary).
## Warning: Removed 2966 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_hline).
## Warning: Removed 36 row(s) containing missing values (geom_path).
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.2310 0.4499 0.5332 0.7307 6.1545
## 1% 99%
## 0.01073855 1.97110425
## Warning: Removed 2908 rows containing non-finite values (stat_summary).
## Warning: Removed 2908 rows containing non-finite values (stat_summary).
## Warning: Removed 2908 rows containing non-finite values (stat_summary).
## Warning: Removed 2908 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_hline).
## Warning: Removed 33 row(s) containing missing values (geom_path).
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.2415 0.4575 0.5328 0.7297 4.0528
## 1% 99%
## 0.01363231 1.87076200
## Warning: Removed 2788 rows containing non-finite values (stat_summary).
## Warning: Removed 2788 rows containing non-finite values (stat_summary).
## Warning: Removed 2788 rows containing non-finite values (stat_summary).
## Warning: Removed 2788 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_hline).
## Warning: Removed 30 row(s) containing missing values (geom_path).
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -0.3531 0.2178 0.4346 0.5171 0.7139 7.2829
## 1% 99%
## 0.007615374 1.906133613
## Warning: Removed 2838 rows containing non-finite values (stat_summary).
## Warning: Removed 2838 rows containing non-finite values (stat_summary).
## Warning: Removed 2838 rows containing non-finite values (stat_summary).
## Warning: Removed 2838 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_hline).
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.2249 0.4360 0.5112 0.7080 5.0495
## 1% 99%
## 0.0103272 1.8120624
## Warning: Removed 2764 rows containing non-finite values (stat_summary).
## Warning: Removed 2764 rows containing non-finite values (stat_summary).
## Warning: Removed 2764 rows containing non-finite values (stat_summary).
## Warning: Removed 2764 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_hline).
## Warning: Removed 2 row(s) containing missing values (geom_path).
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.2329 0.4426 0.5108 0.7067 4.0528
## 1% 99%
## 0.01213521 1.73445644
## Warning: Removed 2666 rows containing non-finite values (stat_summary).
## Warning: Removed 2666 rows containing non-finite values (stat_summary).
## Warning: Removed 2666 rows containing non-finite values (stat_summary).
## Warning: Removed 2666 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_hline).
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.2428 0.4507 0.5132 0.7077 4.0522
## 1% 99%
## 0.01529687 1.66982116
## Warning: Removed 2556 rows containing non-finite values (stat_summary).
## Warning: Removed 2556 rows containing non-finite values (stat_summary).
## Warning: Removed 2556 rows containing non-finite values (stat_summary).
## Warning: Removed 2556 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_hline).
## Warning: Removed 24 row(s) containing missing values (geom_path).
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.2564 0.4604 0.5187 0.7115 3.2967
## 1% 99%
## 0.01969593 1.62313239
## Warning: Removed 2430 rows containing non-finite values (stat_summary).
## Warning: Removed 2430 rows containing non-finite values (stat_summary).
## Warning: Removed 2430 rows containing non-finite values (stat_summary).
## Warning: Removed 2430 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_hline).
## Warning: Removed 20 row(s) containing missing values (geom_path).
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -1.391 1.625 3.373 7.212 7.178 342.646
## 1% 99%
## 0.129259 61.179988
## Warning: Removed 1884 rows containing non-finite values (stat_summary).
## Warning: Removed 1884 rows containing non-finite values (stat_summary).
## Warning: Removed 1884 rows containing non-finite values (stat_summary).
## Warning: Removed 1884 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_hline).
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 1.588 3.238 6.420 6.565 299.327
## 1% 99%
## 0.1404672 51.9021283
## Warning: Removed 1868 rows containing non-finite values (stat_summary).
## Warning: Removed 1868 rows containing non-finite values (stat_summary).
## Warning: Removed 1868 rows containing non-finite values (stat_summary).
## Warning: Removed 1868 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_hline).
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 1.544 3.087 5.674 5.938 266.452
## 1% 99%
## 0.1518194 43.7569696
## Warning: Removed 1842 rows containing non-finite values (stat_summary).
## Warning: Removed 1842 rows containing non-finite values (stat_summary).
## Warning: Removed 1842 rows containing non-finite values (stat_summary).
## Warning: Removed 1842 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_hline).
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 1.503 2.887 4.887 5.316 230.719
## 1% 99%
## 0.1606655 36.1927689
## Warning: Removed 1788 rows containing non-finite values (stat_summary).
## Warning: Removed 1788 rows containing non-finite values (stat_summary).
## Warning: Removed 1788 rows containing non-finite values (stat_summary).
## Warning: Removed 1788 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_hline).
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -1.391 1.593 3.290 7.014 6.960 313.315
## 1% 99%
## 0.1469281 59.1412816
## Warning: Removed 1724 rows containing non-finite values (stat_summary).
## Warning: Removed 1724 rows containing non-finite values (stat_summary).
## Warning: Removed 1724 rows containing non-finite values (stat_summary).
## Warning: Removed 1724 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_hline).
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 1.520 3.030 5.548 5.797 266.452
## 1% 99%
## 0.1646254 42.9779637
## Warning: Removed 1702 rows containing non-finite values (stat_summary).
## Warning: Removed 1702 rows containing non-finite values (stat_summary).
## Warning: Removed 1702 rows containing non-finite values (stat_summary).
## Warning: Removed 1702 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_hline).
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 1.487 2.837 4.766 5.198 230.719
## 1% 99%
## 0.1756477 34.7989555
## Warning: Removed 1658 rows containing non-finite values (stat_summary).
## Warning: Removed 1658 rows containing non-finite values (stat_summary).
## Warning: Removed 1658 rows containing non-finite values (stat_summary).
## Warning: Removed 1658 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_hline).
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 1.487 2.744 4.331 4.843 267.008
## 1% 99%
## 0.1970154 29.4623619
## Warning: Removed 1596 rows containing non-finite values (stat_summary).
## Warning: Removed 1596 rows containing non-finite values (stat_summary).
## Warning: Removed 1596 rows containing non-finite values (stat_summary).
## Warning: Removed 1596 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_hline).
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 1.498 2.703 4.055 4.590 117.370
## 1% 99%
## 0.2263141 26.0516645
## Warning: Removed 1516 rows containing non-finite values (stat_summary).
## Warning: Removed 1516 rows containing non-finite values (stat_summary).
## Warning: Removed 1516 rows containing non-finite values (stat_summary).
## Warning: Removed 1516 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_hline).
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -0.61658 0.01882 0.05373 0.14672 0.12996 51.86043
## 1% 99%
## 0.000000 1.602497
## Warning: Removed 1014 rows containing non-finite values (stat_summary).
## Warning: Removed 1014 rows containing non-finite values (stat_summary).
## Warning: Removed 1014 rows containing non-finite values (stat_summary).
## Warning: Removed 1014 rows containing missing values (geom_point).
## Warning: Removed 107 row(s) containing missing values (geom_path).
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -0.44002 0.02012 0.05279 0.13079 0.12199 36.90202
## 1% 99%
## 0.000000 1.359855
## Warning: Removed 1005 rows containing non-finite values (stat_summary).
## Warning: Removed 1005 rows containing non-finite values (stat_summary).
## Warning: Removed 1005 rows containing non-finite values (stat_summary).
## Warning: Removed 1005 rows containing missing values (geom_point).
## Warning: Removed 91 row(s) containing missing values (geom_path).
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -0.24983 0.02104 0.05139 0.11561 0.11315 28.79895
## 1% 99%
## 0.000000 1.165998
## Warning: Removed 998 rows containing non-finite values (stat_summary).
## Warning: Removed 998 rows containing non-finite values (stat_summary).
## Warning: Removed 998 rows containing non-finite values (stat_summary).
## Warning: Removed 998 rows containing missing values (geom_point).
## Warning: Removed 83 row(s) containing missing values (geom_path).
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -0.10133 0.02189 0.04959 0.09922 0.10230 19.97224
## 1% 99%
## 0.0000000 0.9487073
## Warning: Removed 984 rows containing non-finite values (stat_summary).
## Warning: Removed 984 rows containing non-finite values (stat_summary).
## Warning: Removed 984 rows containing non-finite values (stat_summary).
## Warning: Removed 984 rows containing missing values (geom_point).
## Warning: Removed 74 row(s) containing missing values (geom_path).
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -0.61658 0.01998 0.05513 0.14656 0.13064 51.86043
## 1% 99%
## 0.000000 1.587382
## Warning: Removed 927 rows containing non-finite values (stat_summary).
## Warning: Removed 927 rows containing non-finite values (stat_summary).
## Warning: Removed 927 rows containing non-finite values (stat_summary).
## Warning: Removed 927 rows containing missing values (geom_point).
## Warning: Removed 92 row(s) containing missing values (geom_path).
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -0.24983 0.02200 0.05261 0.11478 0.11359 28.79895
## 1% 99%
## 0.000000 1.110718
## Warning: Removed 922 rows containing non-finite values (stat_summary).
## Warning: Removed 922 rows containing non-finite values (stat_summary).
## Warning: Removed 922 rows containing non-finite values (stat_summary).
## Warning: Removed 922 rows containing missing values (geom_point).
## Warning: Removed 84 row(s) containing missing values (geom_path).
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -0.10133 0.02272 0.05053 0.09814 0.10308 19.97224
## 1% 99%
## 0.0000000 0.9001359
## Warning: Removed 909 rows containing non-finite values (stat_summary).
## Warning: Removed 909 rows containing non-finite values (stat_summary).
## Warning: Removed 909 rows containing non-finite values (stat_summary).
## Warning: Removed 909 rows containing missing values (geom_point).
## Warning: Removed 33 row(s) containing missing values (geom_path).
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -0.05653 0.02334 0.04904 0.08823 0.09579 8.97419
## 1% 99%
## 0.0000000 0.7429484
## Warning: Removed 881 rows containing non-finite values (stat_summary).
## Warning: Removed 881 rows containing non-finite values (stat_summary).
## Warning: Removed 881 rows containing non-finite values (stat_summary).
## Warning: Removed 881 rows containing missing values (geom_point).
## Warning: Removed 31 row(s) containing missing values (geom_path).
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -0.04695 0.02416 0.04825 0.08137 0.09063 8.97419
## 1% 99%
## 0.0000000 0.6456088
## Warning: Removed 836 rows containing non-finite values (stat_summary).
## Warning: Removed 836 rows containing non-finite values (stat_summary).
## Warning: Removed 836 rows containing non-finite values (stat_summary).
## Warning: Removed 836 rows containing missing values (geom_point).
## Warning: Removed 13 row(s) containing missing values (geom_path).